Project-based learning (PjBL) has been increasingly adopted in computer science courses to improve students’ engagement and learning outcomes. Although a computer networks course is in great need of a PjBL course module, no such module is available due to the huge gap between PjBL’s design requirements and the current structure and content of the course. This paper introduces a novel PjBL module for a computer networks course, which challenges the students with a real world problem of developing the communication system for a smart lock. Following the PjBL design principles, we devise several scaffolding activities and assignments, which can be integrated into a semester-long computer networks course. We test ran the PjBL module in both undergraduate- and graduate-level computer networks courses. Our preliminary evaluation results show that the proposed PjBL module is well received by the students and helps improve their learning outcomes.

MOTIVATION: Microbial gene catalogs are data structures that organize genes found in microbial communities, providing a reference for standardized analysis of the microbes across samples and studies. Although gene catalogs are commonly used, they have not been critically evaluated for their effectiveness as a basis for metagenomic analyses. RESULTS: As a case study, we investigate one such catalog, the Integrated Gene Catalog (IGC), however, our observations apply broadly to most gene catalogs constructed to date. We focus on both the approach used to construct this catalog and on its effectiveness when used as a reference for microbiome studies. Our results highlight important limitations of the approach used to construct the IGC and call into question the broad usefulness of gene catalogs more generally. We also recommend best practices for the construction and use of gene catalogs in microbiome studies and highlight opportunities for future research. AVAILABILITY AND IMPLEMENTATION: All supporting scripts for our analyses can be found on GitHub: https://github.com/SethCommichaux/IGC.git. The supporting data can be downloaded from: https://obj.umiacs.umd.edu/igc-analysis/IGC_analysis_data.tar.gz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Metagenomics has revolutionized microbiome research by enabling researchers to characterize the composition of complex microbial communities. Taxonomic profiling is one of the critical steps in metagenomic analyses. Marker genes, which are single-copy and universally found across Bacteria and Archaea, can provide accurate estimates of taxon abundances in the sample. RESULTS: We present TIPP2, a marker gene-based abundance profiling method, which combines phylogenetic placement with statistical techniques to control classification precision and recall. TIPP2 includes an updated set of reference packages and several algorithmic improvements over the original TIPP method. We find that TIPP2 provides comparable or better estimates of abundance than other profiling methods (including Bracken, mOTUsv2 and MetaPhlAn2), and strictly dominates other methods when there are under-represented (novel) genomes present in the dataset. AVAILABILITY AND IMPLEMENTATION: The code for our method is freely available in open-source form at https://github.com/smirarab/sepp/blob/tipp2/README.TIPP.md. The code and procedure to create new reference packages for TIPP2 are available at https://github.com/shahnidhi/TIPP_reference_package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rapid advancements in synthetic biology and nucleic acid synthesis, in particular concerns about its intentional or accidental misuse, call for more sophisticated screening tools to identify genes of interest within short sequence fragments. One major gap in predicting genes of concern is the inadequacy of current tools and ontologies to describe the specific biological processes of pathogenic proteins. The objective of this work is to design software that sensitively assigns taxonomic classifications, functional annotations, and biological processes of interest to short nucleotide sequences of unknown origin (50bp-1,000bp). The overarching goal is to perform sensitive characterization of short sequences and highlight specific pathogenic biological processes of interest (BPoIs). The SeqScreen software executes these tasks in analytical workflows with Nextflow and outputs results in a tab-delimited report. Local and global alignments differentiate hits to taxonomically-related sequences from similar but unrelated sequences, and an ensemble approach leverages multiple tools and databases to assign a variety of functional terms to each query sequence. Final biological process assessments are made from the predicted functional annotations, which leverage information in pre-existing databases, as well as new custom biocurations. Machine learning models predict each biological process of interest on large protein databases before incorporation into the SeqScreen framework to streamline computational efficiency, ensure reproducible results, allow for version control, and facilitate the review of the automated predictions by expert biocurators. The SeqScreen source code is available at https://gitlab.com/treangenlab/seqscreen.

SUMMARY: We developed the metagenomeFeatures R Bioconductor package along with annotation packages for three 16S rRNA databases (Greengenes, RDP and SILVA) to facilitate working with 16S rRNA databases and marker-gene survey feature data. The metagenomeFeatures package defines two classes, MgDb for working with 16S rRNA sequence databases, and mgFeatures for marker-gene survey feature data. The associated annotation packages provide a consistent interface to the different databases facilitating database comparison and exploration. The mgFeatures-class represents a crucial step in the development of a common data structure for working with 16S marker-gene survey data in R. AVAILABILITY AND IMPLEMENTATION: https://bioconductor.org/packages/release/bioc/html/metagenomeFeatures.html. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

The BLAST (Altschul et al., 1990) alignment tool has been the workhorse of genomics research for almost 30 years. While many other tools were developed during this period for performing database searches and sequence alignment, BLAST remains the tool of choice for many use cases, and continues to be actively used in many bioinformatics workflows. Despite the tremendous collective experience of the bioinformatics community with BLAST, the full functionality of this tool remains poorly understood. Here we report on a feature of BLAST that operates in a non-intuitive way and that is frequently misused in bioinformatics workflows, potentially leading to erroneous results impacting numerous scientific articles. Compared to modern search tools, BLAST is highly sensitive across longer evolutionary distances between a query sequence and the database, but also relatively slow. By default, BLAST reports all the database sequences that match a query sequence sufficiently well to within a specified level of quality (usually defined through an E-value cutoff). Bioinformatics workflows usually need to reduce this set to just one or a handful of answers. For example one may estimate the taxonomic origin of a DNA sequence from the taxonomic label associated with the best-scoring database hit(s). In such cases, the slowness of the BLAST tool is further compounded by the computational cost needed to sift through the potentially many hits produced by BLAST for each sequence. To enable the efficient processing of large data sets, researchers frequently rely on shortcuts aimed at reducing the number of BLAST results that need to be processed. A common strategy involves using the ‘-max_target_seqs’ parameter of the NCBI BLAST+ suite. According to the BLAST documentation itself (2008), this parameter represents the ‘number of aligned sequences to keep’. This statement is commonly interpreted as meaning that BLAST will return the top N database hits for a sequence query if the value of max_target_seqs is set to N. For example, in a recent article (Wang et al., 2016) the authors explicitly state ‘Setting “max target seqs” as “1,” only the best match result was considered.’ To our surprise, we have recently discovered that this intuition is incorrect. Instead, BLAST returns the first N hits that exceed the specified E-value threshold, which may or may not be the highest scoring N hits. The invocation using the parameter ‘-max_target_seqs 1’ simply returns the first good hit found in the database, not the best hit as one would assume. Worse yet, the output produced depends on the order in which the sequences occur in the database. For the same query, different results will be returned by BLAST when using different versions of the database even if all versions contain the same best hit for this database sequence. Even ordering the database in a different way would cause BLAST to return a different ‘top hit’ when setting the max_target_seqs parameter to 1. This functionality was first reported as a bug to NCBI by Kumar (2015), and later documented in a blog post (Cock, 2015) by Peter Cock. The functionality remains unchanged to this day, and the BLAST documentation(NCBI, 2008) (last modified in 2016) fails to clarify the misconception a reasonable user would have upon reading the manual. The confusion is further compounded by the fact that in the online BLAST portal, the max_target_seqs parameter behaves in the expected way—the best (rather than first) N hits are returned. The impact of this misunderstanding about the meaning of the BLAST max_target_seqs parameter is likely significant. Hundreds of scientific papers (as determined through a Google Scholar search) explicitly use this parameter to restrict the number of results reported by BLAST, and many more likely rely on this parameter without mentioning it in the main manuscript. Many database search tools justify their performance by comparing directly to BLAST. The use of the max_target_sequences parameter would negatively impact the accuracy of BLAST and artificially inflate the performance of these tools—a serious concern especially as in many cases just a few percentage points distinguish the performance of ‘superior’ tools from that of state of the art approaches such as BLAST. More importantly, the incorrect use of the max_target_seqs parameter can result in invalid analytic results. A biodefense screening tool might miss the presence of Bacillus anthracis simply because a Bacillus cereus sequence occurs before B. anthracis in the database. Similarly, the abundance of Salmonella in food samples may be severely underestimated because many sequences get assigned to a non-pathogenic genome reasonably similar in sequence to Salmonella. Such errors are difficult if not impossible to debug when analyzing complex samples with unknown composition, especially in a production setting. In closing, we encourage the users of BLAST to carefully examine their use of this tool and to avoid the use of the parameter max_target_seqs unless the selected threshold is guaranteed to capture all database hits of interest. We also encourage the team developing BLAST at NCBI to revise the documentation and provide ample warning about unexpected behavior due to this and other parameters of the tool. While one may debate whether the current functionality of the max_target_seqs parameter constitutes a feature or a bug, it is incumbent on the BLAST development team to ensure this functionality is clearly documented, especially after concerns have been raised by users. The authors were supported in part by the US National Science Foundation, Award IIS-1513615. Conflict of Interest: none declared.

An important task in a metagenomic analysis is the assignment of taxonomic labels to sequences in a sample. Most widely used methods for taxonomy assignment compare a sequence in the sample to a database of known sequences. Many approaches use the best BLAST hit(s) to assign the taxonomic label. However, it is known that the best BLAST hit may not always correspond to the best taxonomic match. An alternative approach involves phylogenetic methods which take into account alignments and a model of evolution in order to more accurately define the taxonomic origin of sequences. The similarity-search based methods typically run faster than phylogenetic methods and work well when the organisms in the sample are well represented in the database. On the other hand, phylogenetic methods have the capability to identify new organisms in a sample but are computationally quite expensive. We propose a two-step approach for metagenomic taxon identification; i.e., use a rapid method that accurately classifies sequences using a reference database (this is a filtering step) and then use a more complex phylogenetic method for the sequences that were unclassified in the previous step. In this work, we explore whether and when using top BLAST hit(s) yields a correct taxonomic label. We develop a method to detect outliers among BLAST hits in order to separate the phylogenetically most closely related matches from matches to sequences from more distantly related organisms. We used modified BILD (Bayesian Integral Log Odds) scores, a multiple-alignment scoring function, to define the outliers within a subset of top BLAST hits and assign taxonomic labels. We compared the accuracy of our method to the RDP classifier and show that our method yields fewer misclassifications while properly classifying organisms that are not present in the database. Finally, we evaluated the use of our method as a pre-processing step before more expensive phylogenetic analyses (in our case TIPP) in the context of real 16S rRNA datasets. Our experiments demonstrate the potential of our method to be a filtering step before using phylogenetic methods.

Cloud systems provide a cost-effective service hosting infrastructure for application service providers (ASPs). However, cloud systems are often shared by multiple tenants from different security domains, which makes them vulnerable to various malicious attacks. Moreover, cloud systems often host long-running applications such as massive data processing, which provides more opportunities for attackers to exploit the system vulnerability and perform strategic attacks. In this paper, we present AdapTest, a novel adaptive data-driven runtime service integrity attestation framework for multi-tenant cloud systems. AdapTest can significantly reduce attestation overhead and shorten detection delay by adaptively selecting attested nodes based on dynamically derived trust scores. Our scheme treats attested services as black-boxes and does not impose any special hardware or software requirements on the cloud system or ASPs. We have implemented AdapTest on top of the IBM System S stream processing system and tested it within a virtualized computing cluster. Our experimental results show that AdapTest can reduce attestation overhead by up to 60% and shorten the detection delay by up to 40% compared to previous approaches.

OBJECTIVE: The aim of this study was to rigorously evaluate perceived differences in satisfaction with an electronic health record (EHR) between residents of two medical specialties who share the same health record, practice location, administration, and information technology support. DESIGN: A cross-sectional survey was used comparing user satisfaction between pediatrics residents and internal medicine residents in an academic practice. MEASUREMENTS: The survey was designed to measure baseline demographic characteristics, attitudes toward computers, general satisfaction with an EHR, and perceived practicality of use, variation from familiar practice, organizational support, and impact on delivery of care. RESULTS: Medicine subjects were similar to pediatrics subjects in baseline demographic characteristics. Satisfaction with the EHR implementation was very high for both sets of subjects, but internal medicine residents were significantly less likely to be satisfied with the EHR implementation (relative risk [RR]=0.84, 95% confidence interval [CI]=0.73-0.98) and considerably less likely to believe that their colleagues were satisfied with it (RR=0.56, 95% CI=0.41-0.77). The only surveyed characteristic independently predicting satisfaction was medical specialty (p=0.04). Medicine subjects were less likely to believe template-based documentation improved their efficiency (RR=0.64, 95% CI=0.46-0.88). They were significantly more likely to believe the system had been designed to improve billing (RR=1.50, 95% CI=1.05-2.04) and not to improve patient care (RR=0.61, 95% CI=0.44-0.85). CONCLUSION: The authors found a difference in satisfaction between internal medicine and pediatrics users of an EHR. Although many potential factors that influence satisfaction were similar between subjects in the two specialties, differences in previous experience may have influenced the results. Medicine residents had more previous experience with a different EHR implementation, which they may have perceived as superior to the one involved in this study. Pediatric residents had more previous experience with structured data entry prior to EHR implementation and more preventive care patient encounters for which structured data entry may be well suited. Since successful implementations generally require satisfied users, understanding what factors affect satisfaction can improve chances of a system's success.